The Impacts of Quality-Oriented Dataset Labeling on Tree Cover Segmentation Using U-Net: A Case Study in WorldView-3 Imagery

2023-03-21 | journal article. A publication of Göttingen

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​Jiang T, Freudenberg M, Kleinn C, Ecker A, Nölke N. ​The Impacts of Quality-Oriented Dataset Labeling on Tree Cover Segmentation Using U-Net: A Case Study in WorldView-3 Imagery​. ​​Remote Sensing. ​2023;​15​(6). ​doi:10.3390/rs15061691. 

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Authors
Jiang, Tao; Freudenberg, Maximilian; Kleinn, Christoph; Ecker, Alexander; Nölke, Nils
Abstract
Deep learning has emerged as a prominent technique for extracting vegetation information from high-resolution satellite imagery. However, less attention has been paid to the quality of dataset labeling as compared to research into networks and models, despite data quality consistently having a high impact on final accuracies. In this work, we trained a U-Net model for tree cover segmentation in 30 cm WorldView-3 imagery and assessed the impact of training data quality on segmentation accuracy. We produced two reference tree cover masks of different qualities by labeling images accurately or roughly and trained the model on a combination of both, with varying proportions. Our results show that models trained with accurately delineated masks achieved higher accuracy (88.06%) than models trained on masks that were only roughly delineated (81.13%). When combining the accurately and roughly delineated masks at varying proportions, we found that the segmentation accuracy increased with the proportion of accurately delineated masks. Furthermore, we applied semisupervised active learning techniques to identify an efficient strategy for selecting images for labeling. This showed that semisupervised active learning saved nearly 50% of the labeling cost when applied to accurate masks, while maintaining high accuracy (88.07%). Our study suggests that accurate mask delineation and semisupervised active learning are essential for efficiently generating training datasets in the context of tree cover segmentation from high-resolution satellite imagery.
Issue Date
21-March-2023
Journal
Remote Sensing 
Organization
Fakultät für Forstwissenschaften und Waldökologie ; Burckhardt-Institut ; Abteilung Waldinventur und Fernerkundung ; Institut für Informatik ; Campus-Institut Data Science 
eISSN
2072-4292
Language
English
Sponsor
China scholarship council (CSC)
German Research Foundation (DFG)
Open-Access-Publikationsfonds 2023

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